Flood Fill Algorithm Research Articles

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

MAZE SLOVING ROBOT WITH LIVE MONITORING

This project presents a sophisticated maze-solving robot, equipped with advanced live monitoring capabilities, meticulously designed to autonomously navigate and solve intricate maze environments.The robot's navigation prowess is underpinned by an array of sensors, prominently featuring ultrasonic and infrared technologies, which are adept at detecting and avoiding obstacles while simultaneously mapping the layout of the maze. These sensors feed data into an embedded microcontroller, which processes the information in real-time. To determine the optimal path to the maze's exit, the microcontroller employs sophisticated algorithms such as Depth-First Search (DFS) or Breadth-First Search (BFS), renowned for their efficiency in solving complex mazes.A key feature of the robot is its live monitoring capability, enabled by a wireless communication module. This module is responsible for transmitting critical data, including the robot's status, position, and sensory inputs, to a remote monitoring station. The real-time feedback provided through this system allows users to observe the robot's progress dynamically and make necessary adjustments on the fly, ensuring optimal performance and adaptability in diverse environments. The robot’s compact chassis design ensures superior maneuverability, even in the most confined spaces, while a robust power management system guarantees extended operational periods, making the robot both efficient and reliable.The integration of live monitoring capabilities not only significantly enhances the robot's functionality but also offers invaluable insights into its performance and decision-making processes. This feature is particularly advantageous for educational purposes, providing students and researchers with a tangible demonstration of theoretical concepts in robotics and automation. Additionally, the real-time data acquisition and analysis capabilities make the robot a potent tool for research, especially in fields requiring precise navigation and obstacle avoidance, such as search and rescue operations. Here, the ability to receive immediate feedback and monitor the robot’s actions in real-time is crucial, as it can directly influence the success of missions in dynamic and potentially hazardous environments.The development of this maze-solving robot represents a significant leap forward in the realms of robotics, automation, and remote monitoring technologies. By addressing real-world challenges with enhanced efficiency, this project underscores the potential of advanced robotics to transform a variety of applications. The robot's design, characterized by a harmonious blend of sophisticated sensor arrays, intelligent pathfinding algorithms, and robust real-time monitoring systems, exemplifies the cutting-edge advancements in technology. This convergence of features not only demonstrates the practical applications of theoretical research but also paves the way for future innovations in autonomous systems, highlighting their capability to operate effectively in real-world scenarios and to provide critical insights that can drive further technological progress. Keywords: maze-solving robot, autonomous navigation, live monitoring, ultrasonic sensors, infrared sensors, flood-fill algorithm, real-time processing, wireless communication.

A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B

Abstract. Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.

SpaceWalker enables interactive gradient exploration for spatial transcriptomics data

In spatial transcriptomics (ST) data, biologically relevant features such as tissue compartments or cell-state transitions are reflected by gene expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of 2D and 3D ST data. The user can be guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm identifies transcriptomically similar cells on the fly, based on the high-dimensional data topology. In several use cases, we demonstrate that the spatial projection of these flooded cells highlights tissue architectural features and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology. We also show that SpaceWalker generalizes to several different ST protocols and scales well to large, multi-slice, 3D whole-brain ST data while maintaining real-time interaction performance.

Prevention of enclosed voids in topology optimization using a cumulative sum flood fill algorithm

Topology optimization has seen increased interest with the rise of additive manufacturing (AM) as a fabrication method, because of its ability to exploit the geometric complexity that AM offers. However, AM still imposes some geometric restrictions on the design, most notably on minimum feature size, overhang angles, and enclosed voids. Enclosed voids are problematic because for many AM methods it is impossible to remove supports, unmelted powder or uncured liquid from them. This paper introduces a filter based on a cumulative sum flood fill algorithm to alleviate this issue in a flexible manner. This filter produces a density field where every enclosed void element is rendered solid. It successfully eliminates enclosed voids in both 2D and 3D problems, with low computational cost due to its geometric nature. In addition we demonstrate direct control over the location, amount, and size of powder removal features by varying boundary conditions for the filter, running additional flood fills, and adding morphology operators, respectively.

On-board MRI image compression using video encoder for MR-guided radiotherapy.

Magnetic resonance imaging (MRI) is currently used for online target monitoring and plan adaptation in modern image-guided radiotherapy. However, storing a large amount of data accumulated during patient treatment becomes an issue. In this study, the feasibility to compress MRI images accumulated in MR-guided radiotherapy using video encoders was investigated. Two sorting algorithms were employed to reorder the slices in multiple MRI sets for the input sequence of video encoder. Three cropping algorithms were used to auto-segment regions of interest for separate data storage. Four video encoders, motion-JPEG (M-JPEG), MPEG-4 (MP4), Advanced Video Coding (AVC or H.264) and High Efficiency Video Coding (HEVC or H.265) were investigated. The compression performance of video encoders was evaluated by compression ratio and time, while the restoration accuracy of video encoders was evaluated by mean square error (MSE), peak signal-to-noise ratio (PSNR), and video quality matrix (VQM). The performances of all combinations of video encoders, sorting methods, and cropping algorithms were investigated and their effects were statistically analyzed. The compression ratios of MP4, H.264 and H.265 with both sorting methods were improved by 26% and 5%, 42% and 27%, 72% and 43%, respectively, comparing to those of M-JPEG. The slice-prioritized sorting method showed a higher compression ratio than that of the location-prioritized sorting method for MP4 (P=0.00000), H.264 (P=0.00012) and H.265 (P=0.00000), respectively. The compression ratios of H.265 were improved significantly with the applications of morphology algorithm (P=0.01890 and P=0.00530), flood-fill algorithm (P=0.00510 and P=0.00020) and level-set algorithm (P=0.02800 and P=0.00830) for both sorting methods. Among the four video encoders, H.265 showed the best compression ratio and restoration accuracy. The compression ratio and restoration accuracy of video encoders using inter-frame coding (MP4, H.264 and H.265) were higher than that of video encoders using intra-frame coding (M-JPEG). It is feasible to implement video encoders using inter-frame coding for high-performance MRI data storage in MR-guided radiotherapy.

Remove ambiguous zones using floodfill algorithm in digital photoelasticity

Digital photoelasticity is a widely used technique for the full-field measurement of stress/strain distributions. However, ambiguous zones are present in the isochromatic phase map obtained with the commonly used ten-step phase shifting technique. Obtaining accurate isochromatic phase maps without wrapping can be challenging due to the presence of ambiguous zones. Existing methods to address this issue are complex and not fully automated. In this study, a novel approach is proposed to automatically and accurately remove the ambiguous zones using the Floodfill algorithm, an advanced digital image processing algorithm. The validity and robustness of the proposed method is demonstrated through benchmark tests on a disc and a ring subjected to diametral compression and a four-point bent beam, as well as a model with a complex geometry. The proposed method enhances the automation and simplicity of full-field stress/strain measurement in terms of the removal of ambiguous zones.

Defects Detection in Printed Circuit Boards by Separation and Comparison of Chains

The known K-means clustering, flood-filling, and thinning algorithms are used to find coordinates of contacts in PCB images. Images of different types and colors are considered. The clustering algorithm is used to reduce the number of colors, to get uniform colors in the PCB image. The thinning algorithm is used to build skeletons and find pixels of contacts. The flood-fill algorithm is used to mark and separate chains, defect connectivity, and metal defects. Different subtraction operations are applied to original, transformed, and distributed cumulative histogram images.

Automatic vessel segmentation in MR angiography

Abstract Funding Acknowledgements Type of funding sources: None. Background Vessel-to-background contrast (VBC), the ratio of signal intensities (SI) within a vessel segment compared to the surrounding tissue, is an essential objective parameter for assessing image quality in contrast-enhanced MR angiography (CE-MRA) of peripheral arteries. (1–3) Manual evaluation by the measurement of SIs within the vessel and in the surrounding area is labour-intensive and tedious. (2) An algorithm for automatic segmentation of the vessel lumen and background signal reduces the workload significantly. It has already been successfully applied to CE-MRA in peripheral arterial disease (PAD). (3) However, whether this algorithm is equal to a manual evaluation is not yet known. Purpose To compare the manual and automatic evaluation of VBC in CE-MRA in patients with PAD. Methods A patient with suspected peripheral arterial disease underwent first-pass subtraction MRA on a 3T scanner. Region-of-interests (ROIs) were drawn on predefined vessel segments (Figure 1), manually inside and outside the vessel lumen and additionally a comprehensive ROI with the vessel under investigation in the centre. The algorithm segmented the comprehensive ROI into the vessel and background by identifying a seed point for the vessel and then using a flood fill algorithm. The remaining vessel branches were removed in order to approximate the true background signal. SI of the manual and the automatic segmentation were compared to define the VBC using the formula SI lumen - SI background divided by the standard deviation (SD) of the background for both methods. Results 43 vessel segments were evaluated. An example is given in Figure 2 with A) showing an axial view of the right thigh with a comprehensive ROI with the superficial femoral artery in the centre (green), and manual outlining of the lumen (red) and background (blue). Background signal (B1) and vessel lumen (B2) were used to evaluate the manuall values. Automated segmentation with C1) shows the ROI with the femoral artery in the centre. C2) shows the full extent of the vessel as identified by the flood-fill algorithm, and C3) the resulting mask. Background signal was calculated from SIs remaining in C4 after subtraction of the local maximas shown in C5). The mean SI in the vessel lumen was 4171 (standard deviation, SD 954) for the manual evaluation and 5014 (SD 830) for the automatic evaluation (p = 0.001) and in the background 601 (SD 542) for the manual evaluation and 493 (SD 229) for the automatic evaluation ((p = 0.231). The VBC was 10.3 (SD 5.9) for manual evaluation and 12.2 (SD 5.6) for automatic evaluation (p = 0.139). Conclusion Automatic segmentation of vessel lumen and background signal allows assessment of the VBC in CE-MRA in patients with PAD. The mean SI of the vessel lumen tends to be overestimated about 20% by the automatic algorithm. Higher patient numbers are required to further clarify the applicability of this algorithm for image quality evaluation.

Chains Defects Detection in a Printed Circuit Board Image by the Plane Partition and Flood-filling of Traces

An approach to dividing the printed circuit board into parts to increase visibility of defects in a PCB image is considered. The approach is based on a piecewise linear approximation of a cumulative histogram. The last one is calculated for numbers of informative pixels placed in rows and columns of an image matrix. Informative pixels are those indicating contacts and C traces. The histogram demonstrates a distribution of informative pixels along the OX and OY axes. The beginning and ending points of linear lines are taken as coordinates of the divided parts of the PCB board. The flood-fill algorithm is used to color and separate the PCB chains components. The start pixels are taken from a set of informative pixels. To measure defect values, a mean intensity and gradient functions for the template and manufactured images are used. Distributed cumulative histograms are applied to PCB components to detect places and intensity of defects.

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI.

Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.

An unsupervised image segmentation algorithm for coronary angiography

Computer visual systems can rapidly obtain a large amount of data and automatically process them with ease. These characteristics constitute advantages for the application of such systems in the automatic analysis of medical images, as well as in processing technology. The precision of image segmentation, which plays a critical role in computer visual systems, directly affects the quality of processing results. Coronary angiographs feature various background colors, complex patterns, and blurry edges. The image areas containing blood vessels cannot be precisely segmented through regular methods. Therefore, this study proposed an unsupervised learning algorithm that uses regional parameter expansion (RPE). This method was derived from the flood fill algorithm, which can effectively segment image areas containing blood vessels despite a complex background or uneven light and shadow. An optimal cover tree (OCT) algorithm was proposed for the establishment of coronary arteries and the estimation of vessel diameter. Through the region growing method, spanning trees were used to record the cover length of adjacent connections, thereby establishing vessel paths, and the length can be used to track changes in vessel diameter.

Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm.

Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs.

Design of Multi-Agent Pathfinding Robot Using Improved Flood Fill Algorithm in Maze Exploration

Design of Multi-Agent Pathfinding Robot Using Improved Flood Fill Algorithm in Maze Exploration

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

Landfast ice is an integral component of the coastal ecosystem. Extracting the edge and mapping the extent of landfast ice are one of the main methods for studying ice changes. In this work, a standardized process for extracting landfast ice edge in the Baltic Sea using the InSAR coherence image is established with Sentinel-1 radar data and InSAR technology. A modified approach combining multiscale segmentation and morphological erosion is then proposed to provide a reliable way to extract landfast ice edge. Firstly, the coherence image is obtained using InSAR technology. Then, the edge is separated and extracted with the modified approach. The modified approach is essentially a four-step procedure involving image segmentation, median filter, morphological erosion, and rejection of small patches. Finally, the full extent of landfast ice can be obtained using floodfill algorithm. Multiple InSAR image pairs of Sentinel-1A acquired from 2018 to 2019 are utilized to successfully extract the landfast ice edge in the Gulf of Bothnia. The results show that the landfast ice edge and the extents obtained by the proposed approach are visually consistent with those shown in the ice chart issued by the Swedish Meteorological and Hydrological Institute (SMHI) over a coastline length of 345 km. The mean distance between land–water boundary and the coastline issued by the National Oceanic and Atmospheric Administration (NOAA) is 109.1 m. The modified approach obviously preserves more details in local edge than the reference method. The experimental results show that the modified approach proposed in this paper can extract the edge and map the extent of landfast ice more accurately and quickly, and is therefore expected to contribute to the further understanding and analyzing the changes of landfast ice in the future.

Research on Cell Counting Method Based on Flood Fill Algorithm

Cell counting has a wide range of applications in cytology research and clinical practice. Cell counts are widely used in the early diagnosis and treatment of serious diseases and the screening of effective drugs in the later stage. The method of studying cell counts is of great significance. This paper proposes a method for cell counting based on the highlight area of the cell center as a live cell marker. This method uses algorithms bilateral filtering to smooth noise in the original image; after adaptive threshold segmentation, an algorithm based on flood filling is used. Accurate segmentation of the highlighted area in the center of the cell; finally, counting is achieved through the connected domain labeling algorithm. The experimental results show that the accuracy of the cell counting method proposed in this paper is above 98%, which verifies the feasibility and practicability of the method proposed in this paper.

Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC).

Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.

Phase-field model of precipitation processes with coherency loss

A phase-field model is proposed to simulate coherency loss coupled with microstructure evolution. A special field variable is employed to describe the degree of coherency loss of each particle and its evolution is governed by a Ginzburg-Landau type kinetic equation. For the sake of computational efficiency, a flood-fill algorithm is introduced that can drastically reduce the required number of field variables, which allows the model to efficiently simulate a large number of particles sufficient for characterizing their statistical features during Ostwald ripening. The model can incorporate size dependence of coherency loss, metastability of coherent particles, and effectively incorporate the underlying mechanisms of coherency loss by introducing a so-called differential energy criterion. The model is applied to simulate coarsening of Al3Sc precipitates in aluminum alloy and comprehensively compared with experiments. Our results clearly show how the particle size distribution is changed during coherency loss and affects the coarsening rate.

Image Defogging Framework Using Segmentation and the Dark Channel Prior.

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Extracting the needed portion from a bounded region is an important task in image processing. Editing a map and extracting a region from the map is challenging. It is useful in some contexts to have a region in a separate sheet. In this image processing, we have used the Flood Fill algorithm to extract a region from the image map. To achieve that goal, we had worked in our study to separate a bounded region on a map. Usually, a scanned map may contain a lot of useless information. So we have to process the image to remove useless information from the map. We had quantized the image to a binary one. In the second phase, we have applied a gray color to separate the desired position from a map. Our main objective of the study to extract a bounded region from mapping an image that contains useless information and removes it. We have experimented with several maps and it works successfully.